PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Wind Noise Reduction using Non-negative Sparse Coding
Mikkel N. Schmidt, Jan Larsen and Fu-Tien Hsiao
In: Machine Learning for Signal Processing, IEEE Workshop on (MLSP), 2007(2007).


We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
ID Code:6515
Deposited By:Mikkel Schmidt
Deposited On:08 March 2010